Abstract

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

*Other CHEM models (such as GISS-E2-R [p2] and GFDL-CM3) substantially overestimate observed ozone loss in certain regions and at certain times of year. The fact that some CHEM models underpredict observed ozone loss and others overestimate observed ozone trends helps to explain why we do not find even larger TLS trend differences between the O3+V case (which excludes CHEM models) and the BASE case (which includes CHEM results).

†Previous multimodel studies have found either small (45) or large (47) impacts of stratospheric ozone changes on tropospheric temperature. The ozone-induced tropospheric temperature signals inferred from such multimodel analyses can be obscured by intermodel differences in other applied external forcings and model differences in climate sensitivity (48).

‡Model temperature fields are spatially complete and sampled at uniform time intervals, whereas MSU-based temperature measurements are not spatially complete and not sampled at uniform time intervals. These sampling differences tend to inflate the high-frequency variance of the observations. The RSS percentile realizations attempt to account for this variance inflation (26).

§For each of the four atmospheric layers except TLT, the O3+V fingerprint is searched for in 14 individual observational datasets (RSS v3.3, UAH v5.4, STAR v2.0, and 11 RSS percentile realizations). For TLT, there are only 13 model vs. observed comparisons, because STAR does not provide TLT information. There are, therefore, a total of (3 × 14) + 13 comparisons.

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